IP Library › Granted Patent US 12,724,828
Granted Patent B1
US 12,724,828 · App. 19/423,090 · Granted Sep 1, 2026

Apparatus for and method of generating an interactive dashboard

Inventors: Suzanne Schmitt (Summerville, SC); Jennifer Late (Tysons, VA); Tom West (Falls Church, VA); Eli Wood (Boulder, CO); Keith Pattison (Boulder, CO); Bobby Nicholson (Austin, TX)
Assignee: Longevity Strategists, Inc.
G06F16/9027G06F3/0486G06F16/906
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,724,828
App. No.
19/423,090
Granted
Sep 1, 2026
Kind
B1
Abstract

An apparatus and method for generating an interactive dashboard. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive input data comprising a user profile associated with a user, generate, using a machine learning model, a structured network as a function of the input data. The structured network comprises a plurality of nodes with each node associated with an entity of a plurality of entities. Generating the structured network comprises classifying each node into one or more categories as a function of feature vectors extracted from the input data and entity parameters representing interconnections among the plurality of entities, assigning each node to one or more tasks as a function of the classification, and updating the assignment as a function of supplemental data. An interactive dashboard comprising the structured network is generated.

Claims (69)

1 . An apparatus for generating an interactive dashboard, wherein the apparatus comprises:

at least a computing device, wherein the at least a computing device comprises:

a memory; and

at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to:

receive input data comprising a user profile associated with a user;

generate, using a machine learning model, a structured network as a function of the input data, wherein the structured network comprises a plurality of nodes, each node associated with an entity of a plurality of entities, wherein generating the structured network comprises:

classifying each node into one or more categories as a function of feature vectors extracted from the input data and entity parameters representing interconnections among the plurality of entities;

assigning each node to one or more tasks as a function of the classification, wherein the classification defines a node state associated with each node, the node state comprising classification, active tasks, and verification status;

updating the assignment as a function of supplemental data by modifying both the active tasks and the verification status of the node state; and

storing, in memory, attributes and relationships associated with each node for subsequent retrieval and visualization, wherein the stored attributes and relationships enables querying of relationships and real-time synchronization between a data layer and a visual interface; and

generate, as a function of the node state, an interactive dashboard comprising the structured network, wherein generating the interactive dashboard comprises:

receiving user input events to modify one or more parameters of the structured network; and

rendering, in response to the user input events, a dynamic visualization of the structured network comprising updated node states, including the classification, active tasks, and verification status associated with each node, and relational connections that are synchronized with a data layer.

2 . The apparatus of claim 1 , wherein the at least a processor is further configured to:

receive, using an artificial intelligence assistant, the input data, wherein the input data comprises user input; and

populate one or more fields of the structured network with the user input.

3 . The apparatus of claim 2 , wherein the at least a processor is further configured to:

recommend, using the artificial intelligence assistant, minimum data required for the structured network; and

verify the minimum data recommended by the artificial intelligence assistant as a function of feedback.

4 . The apparatus of claim 1 , wherein the at least a processor is further configured to:

generate interactive interface elements, wherein the interactive interface elements are configured to provide a drag and drop feature for defining the relational connections of each node.

5 . The apparatus of claim 1 , wherein the at least a processor is further configured to:

assign each node a permission level as a function of a predefined protocol.

6 . The apparatus of claim 1 , wherein the at least a processor is further configured to:

log event data associated with modifications of the structured network;

display the modifications of the structured network with a color coded system, wherein a previous version is associated with a first color and a new version is associated with a second color;

verify the modifications of the structured network using an agent; and

display a verification status as a function of an agent review associated with the agent.

7 . The apparatus of claim 1 , wherein the at least a processor is further configured to train the machine learning model on training data comprising labeled feature vectors and labeled relational parameters extracted from historical structured networks.

8 . The apparatus of claim 1 , wherein the at least a processor is further configured to:

instantiate one or more event handlers;

detect, using the one or more event handlers, input signals corresponding to node selection, connection creation, and updates to one or more parameters of the structured network; and

execute, using the one or more event handlers, one or more operations in response to the detected input signals.

9 . The apparatus of claim 8 , wherein the at least a processor is further configured to execute the one or more operations by:

propagating the updates of the structured network to the data layer, wherein the data layer is configured to broadcast the updates to a plurality of connected client devices in real time.

10 . The apparatus of claim 1 , wherein the at least a processor is further configured to adjust weighting parameters of the machine learning model as a function of verified node classifications.

11 . A method of generating an interactive dashboard, wherein the method comprises:

receiving, using at least a processor, input data comprising a user profile associated with a user;

generating, using the at least a processor and a machine learning model, a structured network as a function of the input data, wherein the structured network comprises a plurality of nodes, each node associated with an entity of a plurality of entities, and wherein generating the structured network comprises:

classifying each node into one or more categories as a function of feature vectors extracted from the input data and entity parameters representing interconnections among the plurality of entities;

assigning each node to one or more tasks as a function of the classification, wherein the classification defines a node state associated with each node, the node state comprising classification, active tasks, and verification status;

updating the assignment as a function of supplemental data by modifying both the active tasks and the verification status of the node state; and

storing, in memory, attributes and relationships associated with each node for subsequent retrieval and visualization, wherein the stored attributes and relationships enables querying of relationships and real-time synchronization between a data layer and a visual interface; and

generating, using the at least a processor, as a function of the node state, an interactive dashboard comprising the structured network, wherein generating the interactive dashboard comprises:

receiving user input events to modify one or more parameters of the structured network; and

rendering, in response to the user input events, a dynamic visualization of the structured network comprising updated node states, including the classification, active tasks, and verification status associated with each node, and relational connections that are synchronized with a data layer.

12 . The method of claim 11 , further comprising:

receiving, using an artificial intelligence assistant, the input data, wherein the input data comprises user input; and

populating, using the at least a processor, one or more fields of the structured network with the user input.

13 . The method of claim 12 , further comprising:

recommending, using the artificial intelligence assistant, minimum data required for the structured network; and

verifying, using the at least a processor, the minimum data recommended by the artificial intelligence assistant as a function of feedback.

14 . The method of claim 11 , further comprising:

generating, using the at least a processor, interactive interface elements, wherein the interactive interface elements are configured to provide a drag and drop feature for defining the relational connections of each node.

15 . The method of claim 11 , further comprising:

assigning, using the at least a processor, each node a permission level as a function of a predefined protocol.

16 . The method of claim 11 , further comprising:

logging, using the at least a processor, event data associated with modifications of the structured network;

displaying, using the at least a processor, the modifications of the structured network with a color coded system, wherein a previous version is associated with a first color and a new version is associated with a second color;

verifying, using the at least a processor, the modifications of the structured network using an agent; and

displaying, using the at least a processor, a verification status as a function of an agent review associated with the agent.

17 . The method of claim 11 , further comprising training, using the at least a processor, the machine learning model on training data comprising labeled feature vectors and labeled relational parameters extracted from historical structured networks.

18 . The method of claim 11 , further comprising:

instantiating, using the at least a processor, one or more event handlers;

detecting, using the one or more event handlers, input signals corresponding to node selection, connection creation, and updates to one or more parameters of the structured network; and

executing, using the one or more event handlers, one or more operations in response to the detected input signals.

19 . The method of claim 18 , further comprising executing, using the at least a processor, the one or more operations by:

propagating the updates of the structured network to the data layer, wherein the data layer is configured to broadcast the updates to a plurality of connected client devices in real time.

20 . The method of claim 11 , further comprising adjusting, using the at least a processor, weighting parameters of the machine learning model as a function of verified node classifications.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 19, 2026
From: SCHMITT, SUZANNE; LATE, JENNIFER; WEST, TOM; WOOD, ELI; PATTISON, KEITH; NICHOLSON, BOBBY
To: LONGEVITY STRATEGISTS, INC.
Reel/Frame 074690/0981 →
References Cited (7)
US 10489457B1 · Wulf · 2019 [cited by examiner]
US 11687438B1 · Torbett · 2023 [cited by examiner]
US 12541555B2 · Vadapandeshwara · 2026 [cited by examiner]
US 20180032216A1 · Naous · 2018 [cited by applicant]
US 20190363959A1 · Rice · 2019 [cited by examiner]
US 20210342344A1 · Kowolenko et al. · 2021 [cited by applicant]
US 20230237096A1 · Bullard et al. · 2023 [cited by applicant]